Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
1. A computer-implemented method for generating and simulating a computed tomography (CT) protocol, comprising: receiving, via a graphical user interface, at a processor user input comprising patient population size settings and scan technique settings for modeling the effects of the scan technique settings across a patient population as a function of patient size; generating, via the processor, a patient population profile based on at least the patient population size settings and the scan technique settings, wherein the patient population profile comprises specific CT scan technique settings to be applied across different size ranges of the patient population as a function of patient size; and displaying, on the graphical user interface, one or more visualization elements illustrating the effect of these specific CT scan technique settings on specific imaging metrics across the patient population.
This invention relates to a computer-implemented method for generating and simulating computed tomography (CT) protocols to optimize scan techniques across diverse patient populations. The method addresses the challenge of tailoring CT scan parameters to varying patient sizes, ensuring consistent image quality while minimizing radiation exposure. The system receives user inputs defining patient population size distributions and scan technique settings, such as tube voltage, current, and reconstruction algorithms. A processor then generates a patient population profile, which maps specific CT scan techniques to different patient size ranges. The profile ensures that scan parameters are dynamically adjusted based on patient size to maintain optimal imaging metrics, such as image noise, contrast, and radiation dose. The method further visualizes the effects of these settings on imaging performance across the population, allowing users to assess trade-offs and refine protocols before clinical implementation. This approach enables radiologists and technicians to design and validate CT protocols that balance diagnostic accuracy with patient safety, particularly in large-scale or multi-center studies where patient demographics vary widely.
2. The computer-implemented method of claim 1 , wherein the user input comprises clinical task information.
This invention relates to computer-implemented methods for processing user input in a clinical or medical context. The method involves receiving user input that includes clinical task information, such as details about medical procedures, diagnoses, or treatment plans. The system then processes this input to generate or update a clinical workflow, ensuring that the tasks are properly documented, tracked, and executed within a healthcare environment. The method may also involve validating the input against predefined clinical protocols or guidelines to ensure accuracy and compliance. Additionally, the system may integrate with electronic health record (EHR) systems to automatically update patient records or trigger follow-up actions based on the clinical task information. The invention aims to improve efficiency and accuracy in clinical workflows by automating the capture and processing of task-related data, reducing manual errors, and ensuring adherence to medical standards. The method may also support collaboration among healthcare providers by sharing task updates in real-time. The overall goal is to streamline clinical operations while maintaining high standards of patient care.
3. The computer-implemented method of claim 2 , wherein the clinical task information comprises an expected relative level of contrast in a scan.
This invention relates to medical imaging and the automated analysis of scan data to assist in clinical decision-making. The problem addressed is the need for improved methods to analyze and interpret medical scans, particularly in determining the expected relative level of contrast in a scan. Contrast levels are critical for diagnosing conditions such as tumors, vascular abnormalities, or tissue damage, but manual assessment is time-consuming and prone to variability. The method involves a computer-implemented process that processes scan data to extract clinical task information, specifically the expected relative contrast level. This is achieved by analyzing the scan data to identify regions of interest and comparing their intensity values to surrounding tissues. The system may use machine learning models trained on labeled datasets to predict contrast levels based on features such as pixel intensity, texture, and spatial relationships. The method may also incorporate prior knowledge, such as anatomical landmarks or known disease patterns, to refine the contrast assessment. Additionally, the method may adjust the analysis based on scan parameters, such as imaging modality (e.g., MRI, CT) or acquisition settings, to ensure accuracy. The output provides clinicians with a quantitative or qualitative assessment of contrast, aiding in diagnosis, treatment planning, or monitoring disease progression. This automation reduces human error and improves efficiency in medical imaging workflows.
4. The computer-implemented method of claim 2 , wherein the one or more visualization elements illustrate a variation in the specific imaging metrics across the patient population as a function of patient size, clinical task, and a combination of patient size and clinical task.
This invention relates to medical imaging and data visualization, specifically addressing the challenge of effectively presenting imaging performance metrics across diverse patient populations. The method involves generating visualization elements that depict variations in specific imaging metrics based on patient size, clinical task, and combinations of both factors. These visualizations help clinicians and researchers assess how imaging performance varies depending on patient characteristics and the intended diagnostic or procedural use. The underlying system collects imaging data from multiple patients, processes it to extract relevant metrics, and then generates visual representations that highlight trends, outliers, or correlations. By analyzing these visualizations, users can identify how different patient sizes (e.g., body mass, organ dimensions) or clinical tasks (e.g., diagnostic imaging, interventional procedures) influence imaging quality, accuracy, or efficiency. The method supports comparative analysis, enabling users to evaluate imaging protocols, equipment, or techniques across different patient groups. This approach enhances decision-making by providing clear, data-driven insights into imaging performance variability, ultimately improving diagnostic reliability and patient care.
5. The computer-implemented method of claim 1 , wherein the specific imaging metrics comprise dose specific metrics, image quality metrics, or both dose specific and image quality metrics.
This invention relates to computer-implemented methods for evaluating imaging systems, particularly in medical or industrial applications where radiation dose and image quality are critical. The method addresses the challenge of optimizing imaging protocols by analyzing specific metrics that quantify both radiation dose and image quality. These metrics may include dose-specific measurements, such as radiation exposure levels, and image quality assessments, such as resolution, noise, or artifact presence. The method processes imaging data to extract these metrics, enabling users to compare different imaging protocols or configurations to determine the most effective balance between dose reduction and image clarity. By providing quantitative evaluations, the method supports decision-making in clinical or industrial settings where minimizing radiation exposure while maintaining diagnostic or analytical accuracy is essential. The invention may be applied to various imaging modalities, including X-ray, CT, or other radiation-based systems, to enhance safety and performance.
6. The computer-implemented method of claim 1 , wherein the patient population size settings comprise different size ranges and a preferred scan technique for each respective size range.
This invention relates to medical imaging, specifically optimizing scan techniques for different patient populations based on size. The problem addressed is the need to tailor imaging protocols to patient size to improve image quality, reduce radiation exposure, and enhance diagnostic accuracy. The method involves defining patient population size settings that include multiple size ranges, with each range having an associated preferred scan technique. These settings ensure that the imaging system automatically selects the most appropriate technique for a given patient size, improving efficiency and consistency in clinical practice. The method may also involve adjusting scan parameters such as radiation dose, image resolution, and acquisition time based on the selected size range. By standardizing scan techniques across different patient sizes, the invention aims to reduce variability in imaging outcomes and enhance patient safety. The system may integrate with existing imaging modalities like CT, MRI, or X-ray to apply these optimized techniques during routine scans. This approach helps radiologists and technicians streamline workflow while ensuring high-quality diagnostic images tailored to individual patient needs.
7. The computer-implemented method of claim 6 , comprising displaying, on the graphical user interface, a graph representing respective proportions of the patient population to be imaged with a respective scan technique or a proportion of a respective size range to be imaged with the respective scan technique.
This invention relates to medical imaging systems, specifically optimizing scan techniques for patient populations. The problem addressed is efficiently selecting appropriate imaging protocols for diverse patient groups, balancing image quality, radiation dose, and operational efficiency. The method involves analyzing patient data to determine optimal scan techniques for different subgroups, such as varying body sizes or medical conditions. A graphical user interface displays a graph showing the distribution of patients assigned to each scan technique, allowing radiologists to visualize how imaging resources are allocated. The graph may also represent the proportion of patients within specific size ranges assigned to particular techniques, helping identify trends or inefficiencies. This visualization aids in refining protocols to improve patient outcomes and resource utilization. The system may integrate with existing imaging equipment and electronic health records to automate technique selection based on predefined criteria. The goal is to standardize imaging practices while accommodating individual patient needs, reducing variability in scan quality and dose exposure.
8. The computer-implemented method of claim 1 , comprising: receiving, at the processor, a radiograph localizer of an individual patient; receiving, via the graphical user interface, at the processor additional scan technique settings, wherein some of the additional scan techniques are derived from the patient population profile; and generating, via the processor, and displaying, via the graphical user interface, a simulated image of the individual patient based on the radiograph localizer and the additional scan technique settings.
This invention relates to medical imaging, specifically a computer-implemented method for generating simulated radiographic images tailored to individual patients. The method addresses the challenge of optimizing scan parameters for radiography by leveraging patient-specific data and population-based profiles to produce accurate, personalized simulated images before actual scanning occurs. The method involves receiving a radiograph localizer, which is a preliminary image or data set of an individual patient, and additional scan technique settings. These settings may include parameters like exposure levels, contrast, and other imaging factors. Some of these settings are derived from a patient population profile, which aggregates data from similar patients to inform optimal scan techniques. The method then processes this input using a processor to generate a simulated radiographic image, which is displayed via a graphical user interface. This simulation allows healthcare professionals to preview the expected image quality and adjust settings before performing the actual scan, improving efficiency and reducing unnecessary radiation exposure. The system integrates patient-specific data with broader population trends to enhance imaging accuracy and personalization, ensuring that each scan is optimized for the individual while benefiting from collective knowledge. This approach supports better diagnostic planning and resource utilization in medical imaging workflows.
9. The computer-implemented method of claim 8 , wherein the additional scan technique settings comprise target image quality settings or target dose settings, primary image reconstruction settings, X-ray tube kilovoltage settings, X-ray tube current settings, scan specific settings, and/or scout specific settings.
This invention relates to medical imaging systems, specifically computed tomography (CT) scanners, and addresses the challenge of optimizing scan parameters to balance image quality, radiation dose, and operational efficiency. The method involves adjusting scan technique settings based on predefined targets, such as image quality or radiation dose, to improve diagnostic accuracy while minimizing patient exposure. Key settings include primary image reconstruction parameters, X-ray tube kilovoltage and current, scan-specific configurations (e.g., slice thickness, rotation speed), and scout-specific adjustments (e.g., pre-scan localization). The system dynamically selects these parameters to meet desired performance criteria, ensuring consistent imaging outcomes across different patient anatomies and clinical requirements. By automating the optimization process, the invention reduces manual intervention, enhances workflow efficiency, and ensures compliance with safety standards. The method is particularly useful in clinical settings where precise imaging is critical, such as oncology, cardiology, and trauma care. Prior art in this domain may include adaptive dose modulation techniques, AI-driven parameter selection, and rule-based optimization algorithms for CT imaging.
10. A non-transitory computer-readable medium, the computer-readable medium comprising processor-executable code that when executed by a processor, causes the processor to: receive, via a graphical user interface, user input comprising patient population size settings and scan technique settings for modeling the effects of the scan technique settings across a patient population as a function of patient size; generate a patient population profile based on at least the patient population size settings and the scan technique settings, wherein the patient population profile comprises specific CT scan technique settings to be applied across different size ranges of the patient population as a function of patient size; and display, on the graphical user interface, one or more visualization elements illustrating the effect of these specific CT scan technique settings on specific imaging metrics across the patient population.
This invention relates to medical imaging, specifically optimizing computed tomography (CT) scan techniques across diverse patient populations. The problem addressed is the variability in imaging quality and radiation dose when applying uniform scan settings to patients of different sizes. The solution involves a software system that models and visualizes the effects of scan technique settings across a patient population as a function of patient size. The system receives user input defining patient population size distributions and scan technique parameters (e.g., tube voltage, current, or exposure time). It then generates a patient population profile that tailors specific CT scan settings for different patient size ranges. This profile ensures that imaging metrics (e.g., image quality, noise levels, or radiation dose) are optimized for each size group. The system displays visualizations (e.g., graphs or charts) showing how these tailored settings impact imaging metrics across the population, enabling users to assess trade-offs and refine techniques. By dynamically adjusting scan parameters based on patient size, the invention improves imaging consistency and safety while reducing unnecessary radiation exposure. The graphical interface allows radiologists or technicians to interactively explore the effects of different settings before implementation. This approach enhances personalized imaging protocols without requiring manual adjustments for each patient.
11. The non-transitory computer readable storage medium of claim 10 , wherein the user input comprises clinical task information.
This invention relates to a computer-implemented system for processing clinical task information. The system addresses the challenge of efficiently managing and analyzing clinical data, particularly in healthcare environments where tasks such as diagnosis, treatment planning, and patient monitoring require precise and timely data handling. The system includes a non-transitory computer-readable storage medium containing instructions that, when executed by a processor, perform operations to process user input related to clinical tasks. The user input may include various types of clinical task information, such as patient data, diagnostic results, treatment protocols, or other relevant medical information. The system is designed to receive, store, and analyze this input to support clinical decision-making. The system may also include a user interface for inputting and displaying clinical task information, ensuring that healthcare professionals can easily interact with the system. Additionally, the system may incorporate data processing modules to extract, transform, and load clinical data into a structured format, enabling efficient retrieval and analysis. The system may further include machine learning or artificial intelligence components to predict outcomes, suggest treatments, or identify patterns in clinical data. By integrating these features, the system aims to improve the accuracy, speed, and reliability of clinical task management, ultimately enhancing patient care and operational efficiency in healthcare settings. The invention is particularly useful in environments where large volumes of clinical data must be processed and analyzed in real-time.
12. The non-transitory computer readable storage medium of claim 11 , wherein the clinical task information comprises an expected amount of contrast in a scan.
In medical imaging, accurately determining the amount of contrast agent required for a scan is critical for diagnostic quality while minimizing patient risk. Excessive contrast can cause adverse reactions, while insufficient contrast may lead to poor image quality. This invention addresses the challenge by providing a system that analyzes clinical task information, including the expected amount of contrast needed for a specific scan, to optimize imaging protocols. The system processes this information to generate a tailored imaging protocol that ensures the correct contrast dosage is administered, improving diagnostic accuracy and patient safety. The clinical task information may include details such as the type of scan, patient-specific factors, and imaging objectives, which are used to calculate the optimal contrast amount. By integrating this data into the imaging workflow, the system automates the adjustment of contrast levels, reducing manual errors and enhancing efficiency in medical imaging procedures. The invention is particularly useful in radiology departments where precise contrast administration is essential for high-quality diagnostic imaging.
13. The non-transitory computer readable storage medium of claim 10 , wherein the one or more visualization elements illustrate a variation in the specific imaging metrics across the patient population as a function of patient size, clinical task, and a combination of patient size and clinical task.
This invention relates to medical imaging and the analysis of imaging metrics across patient populations. The problem addressed is the need to visualize variations in specific imaging metrics based on patient size, clinical task, and their combination. The solution involves a non-transitory computer-readable storage medium containing instructions for generating visualizations that display how these metrics change across different patient sizes, clinical tasks, or both. The system collects imaging data from multiple patients, processes it to extract relevant metrics, and then generates visualizations that highlight trends or patterns. These visualizations help clinicians and researchers understand how imaging performance varies with patient characteristics and task requirements. The approach allows for comparative analysis, enabling improvements in imaging protocols, device calibration, or diagnostic accuracy. The system may also support filtering or segmentation of data to focus on specific subsets of patients or tasks. By providing clear, data-driven insights, the invention aids in optimizing medical imaging workflows and outcomes.
14. The non-transitory computer readable storage medium of claim 10 , wherein the specific imaging metrics comprise dose specific metrics, image quality metrics, or both dose specific and image quality metrics.
This invention relates to medical imaging systems, specifically improving the evaluation of imaging performance by analyzing specific metrics related to radiation dose and image quality. The system captures imaging data from a medical imaging device, such as an X-ray or CT scanner, and processes this data to extract metrics that quantify radiation dose delivered to a patient and the resulting image quality. These metrics may include dose-specific measurements (e.g., radiation exposure levels) and image quality assessments (e.g., resolution, noise, or artifact presence). The system then compares these metrics against predefined thresholds or historical data to assess whether the imaging procedure meets safety and quality standards. If deviations are detected, the system may trigger alerts or adjustments to optimize future imaging protocols. The invention aims to enhance patient safety by minimizing unnecessary radiation exposure while ensuring diagnostic image quality. The system may also integrate with existing imaging workflows, allowing radiologists and technicians to review metric trends and make data-driven decisions. By automating the analysis of dose and image quality metrics, the invention reduces manual review efforts and improves consistency in medical imaging evaluations.
15. The non-transitory computer readable storage medium of claim 10 , wherein the patient population size settings comprise different size ranges and a preferred scan technique for each respective size range.
This invention relates to medical imaging systems, specifically optimizing scan techniques for different patient populations. The problem addressed is the need to tailor imaging protocols to varying patient sizes to improve image quality and reduce radiation exposure. The system includes a non-transitory computer-readable storage medium storing instructions that, when executed, configure a medical imaging device to adjust scan parameters based on predefined patient population size settings. These settings include multiple size ranges, each associated with a preferred scan technique. The system determines the appropriate size range for a patient and applies the corresponding scan technique, ensuring optimal imaging conditions. The invention also involves generating a patient-specific scan protocol by selecting parameters such as tube voltage, current, and exposure time based on the patient's size range. This approach enhances diagnostic accuracy while minimizing unnecessary radiation exposure. The system may also include user interfaces for configuring or modifying the size ranges and associated scan techniques, allowing customization for different clinical settings. The invention improves efficiency in medical imaging by automating protocol selection, reducing manual adjustments, and ensuring consistent imaging quality across diverse patient populations.
16. The non-transitory computer readable storage medium of claim 15 , wherein the processor is further caused to display, on the graphical user interface, a graph representing respective proportions of the patient population to be imaged with a respective scan technique or a proportion of a respective size range to be imaged with the respective scan technique.
This invention relates to medical imaging systems, specifically optimizing scan techniques for patient populations. The problem addressed is efficiently selecting appropriate imaging protocols for diverse patient groups to balance image quality, radiation dose, and resource utilization. The system analyzes patient data, such as size or condition, to categorize individuals into groups and assigns optimal scan techniques to each group. A graphical user interface displays a graph showing the distribution of patients across different scan techniques or size ranges, enabling radiologists to visualize how imaging resources are allocated. The system may also adjust scan parameters in real-time based on patient-specific data to improve efficiency and safety. The invention aims to standardize imaging protocols while accommodating individual patient needs, reducing unnecessary radiation exposure and optimizing workflow. The graphical representation helps clinicians assess the impact of different scan techniques on patient populations, supporting data-driven decision-making in radiology departments.
17. The non-transitory computer readable storage medium of claim 10 , wherein the processor is further caused to: receive a radiograph localizer of an individual patient; receive, via the graphical user interface, additional scan technique settings, wherein some of the additional scan techniques are derived from the patient population profile; and generate and display, via the graphical user interface, a simulated image of the individual patient based on the radiograph localizer and the additional scan technique settings.
This invention relates to medical imaging systems that generate simulated patient images based on radiograph localizers and scan technique settings. The technology addresses the challenge of optimizing imaging protocols for individual patients by leveraging population-based scan techniques while allowing customization for specific cases. The system receives a radiograph localizer, which is a preliminary X-ray image used to position and plan a subsequent scan. It also accepts additional scan technique settings, some of which are derived from a patient population profile. This profile likely includes historical imaging data and protocols from similar patients, enabling the system to suggest optimized settings. The user can adjust these settings via a graphical interface. Using the radiograph localizer and the selected scan techniques, the system generates a simulated image of the individual patient. This simulation helps clinicians preview the expected scan quality and coverage before performing the actual imaging procedure, reducing the need for retakes and improving efficiency. The system may also allow further refinement of scan parameters based on the simulated output, ensuring the final scan meets clinical requirements. The invention enhances medical imaging workflows by combining population-derived techniques with patient-specific adjustments, improving accuracy and reducing radiation exposure.
18. The non-transitory computer readable storage medium of claim 10 , wherein the additional scan technique settings comprise target image quality settings, target dose settings, primary image reconstruction settings, X-ray tube kilovoltage settings, X-ray tube current settings, scan specific settings, and/or scout specific settings.
This invention relates to medical imaging systems, specifically computed tomography (CT) scanners, and addresses the challenge of optimizing scan parameters to balance image quality, radiation dose, and system performance. The invention provides a non-transitory computer-readable storage medium containing instructions for adjusting scan technique settings in real-time during a CT scan. These settings include target image quality parameters, radiation dose limits, primary image reconstruction algorithms, X-ray tube kilovoltage (kV) and current (mA) settings, scan-specific configurations, and scout scan adjustments. The system dynamically modifies these parameters based on patient-specific factors, anatomical regions, or operator preferences to enhance diagnostic accuracy while minimizing unnecessary radiation exposure. The medium also stores historical scan data to refine future parameter adjustments. This approach improves workflow efficiency by automating parameter optimization, reducing manual adjustments, and ensuring consistent imaging outcomes across different patients and scan types. The invention is particularly useful in clinical settings where rapid, high-quality imaging is critical, such as emergency departments or routine diagnostic procedures.
19. A system, comprising: a display; and a processor configured to execute instructions to: receive, via a graphical user interface, user input comprising patient population size settings and scan technique settings for modeling the effects of the scan technique settings across a patient population as a function of patient size; generate a patient population profile based on at least the patient population size settings and the scan technique settings, wherein the patient population profile comprises specific CT scan technique settings to be applied across different size ranges of the patient population as a function of patient size; and display, on the graphical user interface, one or more visualization elements illustrating the effect of these specific CT scan technique settings on specific imaging metrics across the patient population.
This system relates to medical imaging, specifically optimizing computed tomography (CT) scan techniques for diverse patient populations. The problem addressed is the variability in imaging quality and radiation exposure across patients of different sizes when using standardized scan settings. The system provides a solution by modeling and visualizing the effects of scan technique settings across a range of patient sizes. The system includes a display and a processor that executes instructions to receive user input defining patient population size distributions and scan technique parameters. These inputs are used to generate a patient population profile, which assigns specific CT scan settings (e.g., tube voltage, current, or exposure time) to different patient size ranges. The system then simulates how these settings impact imaging metrics (e.g., image noise, contrast, or radiation dose) across the population. Visualization elements, such as graphs or charts, are displayed to illustrate these effects, allowing users to assess and refine scan techniques for optimal performance across varying patient sizes. This approach ensures personalized imaging protocols that balance image quality and radiation safety for different patient demographics.
20. The system of claim 19 , wherein the processor is further configured to: receive a radiograph localizer of an individual patient; receive, via the graphical user interface, additional scan technique settings, wherein some of the additional scan techniques are derived from the patient population profile; and generate and display, via the graphical user interface, a simulated image of the individual patient based on the radiograph localizer and the additional scan technique settings.
This invention relates to medical imaging systems that optimize scan techniques for individual patients based on population data. The problem addressed is the variability in imaging quality and radiation exposure due to inconsistent scan settings across patients with similar anatomical characteristics. The system includes a processor and a graphical user interface (GUI) that receives a radiograph localizer of an individual patient, which is an initial X-ray image used to position the patient for a subsequent scan. The processor also receives additional scan technique settings, some of which are derived from a patient population profile. This profile contains aggregated imaging data from previous patients with similar characteristics, allowing the system to suggest optimized settings for the current patient. The system then generates and displays a simulated image of the individual patient based on the radiograph localizer and the additional scan technique settings. This simulation helps clinicians preview the expected scan quality and adjust settings before performing the actual scan, improving efficiency and reducing unnecessary radiation exposure. The GUI allows users to interact with the system, inputting or modifying scan parameters and visualizing the simulated results. The overall goal is to standardize and personalize imaging protocols, ensuring consistent and high-quality diagnostic images while minimizing risks.
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October 1, 2019
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